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• W2725422033 abstract "This work makes the first attempt to generate articulated human motion sequence from a single image. On one hand, we utilize paired inputs including human skeleton information as motion embedding and a single human image as appearance reference, to generate novel motion frames based on the conditional GAN infrastructure. On the other hand, a triplet loss is employed to pursue appearance smoothness between consecutive frames. As the proposed framework is capable of jointly exploiting the image appearance space and articulated/kinematic motion space, it generates realistic articulated motion sequence, in contrast to most previous video generation methods which yield blurred motion effects. We test our model on two human action datasets including KTH and Human3.6M, and the proposed framework generates very promising results on both datasets." @default.
• W2725422033 created "2017-07-14" @default.
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• W2725422033 date "2017-10-19" @default.
• W2725422033 modified "2023-10-02" @default.
• W2725422033 title "Skeleton-Aided Articulated Motion Generation" @default.
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• W2725422033 doi "https://doi.org/10.1145/3123266.3123277" @default.
• W2725422033 hasPublicationYear "2017" @default.
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